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Proceedings Paper

How much agreement is there in the visual search strategy of experts reading mammograms?
Author(s): Claudia Mello-Thoms
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Paper Abstract

Previously we have shown that the eyes of expert breast imagers are attracted to the location of a malignant mass in a mammogram in less than 2 seconds after image onset. Moreover, the longer they take to visually fixate the location of the mass, the less likely it is that they will report it. We conjectured that this behavior was due to the formation of the initial hypothesis about the image (i.e., 'normal' - no lesions to report, or 'abnormal' - possible lesions to report). This initial hypothesis is formed as a result of a difference template between the experts' expectations of the image and the actual image. Hence, when the image is displayed, the expert detects the areas that do not correspond to their 'a priori expectation', and these areas get assigned weights according to the magnitude of the perturbation. The radiologist then uses eye movements to guide the high resolution fovea to each of these locations, in order to resolve each perturbation. To accomplish this task successfully the radiologist uses not only the local features in the area but also lateral comparisons with selected background locations, and this comprises the radiologist's visual search strategy. Eye-position tracking studies seem to suggest that no two radiologists search the breast parenchyma alike, which makes one wonder whether successful search models can be developed. In this study we show that there is more to the experts' search strategy than meets the eye.

Paper Details

Date Published: 6 March 2008
PDF: 9 pages
Proc. SPIE 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment, 691704 (6 March 2008); doi: 10.1117/12.768835
Show Author Affiliations
Claudia Mello-Thoms, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 6917:
Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment
Berkman Sahiner; David J. Manning, Editor(s)

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